Homology Modeling I. Growth of the Protein Data Bank PDB. Basel, September 30, EMBnet course: Introduction to Protein Structure Bioinformatics

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1 Swiss Institute of Bioinformatics EMBnet course: Introduction to Protein Structure Bioinformatics Homology Modeling I Basel, September 30, 2004 Torsten Schwede Biozentrum - Universität Basel Swiss Institute of Bioinformatics Klingelbergstr CH Basel, Switzerland Tel: Growth of the Protein Data Bank PDB [ PDB: ]

2 Public Database Holdings 1'000' '000 No experimental structure for most sequences 10'000 1'000 TrEMBL SwissProt PDB MNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITK DEAEKLFNQD VDAAVRGILR NAKLKPVYDS LDAVRRCALI NMVFQMGETG VAGFTNSLRM LQQKRWDEAA VNLAKSRWYN QTPNRAKRVI TTFRTGTWDA YKNL Many proteins fold spontaneously to their native structure Protein folding is relatively fast (nsec sec) Chaperones speed up folding, but do not alter the structure The protein sequence contains all information needed to create a correctly folded protein. Can we predict protein structures from protein sequences alone (ab initio)?

3 ν = bonds angles torsions Molecular Dynamics ki 2 ki 2 VN 2 ( l l ) ( θ θ ) i i i,0 i,0 ( 1+ cos( nω γ )) N N σ ij σ ij qiq j = + 4πεij + i 1 j i 1 rij rij 4πε 0rij = Ab initio protein folding simulation Physical time for simulation 10 4 seconds Typical time-step size seconds Number of MD time steps Atoms in a typical protein and water simulation Approximate number of interactions in force calculation 10 9 Machine instructions per force calculation 1000 Total number of machine instructions BlueGene capacity (floating point operations per second) 1 petaflop (10 15 ) Blue Gene will need 1-3 years to simulate 100 µsec. [ ]

4 Rosetta Stone Approach David Baker group Find sequence patterns that strongly correlate with protein structure at the local level to create a library of fragments (Isites). E.g. amphipathic helix : Amino acid statistics Helix position Rosetta Stone Approach To build a model building for a new sequence: Search for compatible fragments (reduced alphabet) Use Monte Carlo simulated annealing to assemble overlapping fragments Scoring functions are used to select best models (~1000)

5 Rosetta Stone Approach Generates thousands of models Best Models in CASP4: ~ 5 10 Å rmsd Ca Difficult to distinguish good and bad models The number of different protein folds is limited: Already known folds PDB submissions per year New folds Year

6 Evolution of the globin family: Evolution of protein structure families Rmsd of backbone atoms in core Percent identical residues in core [ Chothia & Lesk (1986) ] Common core = all residues that can be superposed in 3D For proteins > 60% identical residues, the core contains > 90 % of all residues deviating less than 1.0 Å.

7 .. Sequence similarity implies structural similarity? 100 identity Percentage sequence identity/similarity Don t know Sequence identity implies structural similarity region... (B.Rost, Columbia, NewYork) Number of residues aligned Sequence similarity implies structural similarity? Percentage sequence identity/similarity Don t know Sequence identity implies structural similarity region... identity similarity (B.Rost, Columbia, NewYork) Number of residues aligned

8 Similar Sequence Similar Structure Homology modeling = Comparative protein modeling = Knowledge-based modeling Idea: Using experimental 3D-structures of related family members (templates) to calculate a model for a new sequence (target). Comparative Modeling Known Structures (Templates) Target Sequence Template Selection Alignment Template - Target Structure Evaluation & Assessment Structure modeling Homology Model(s)

9 Comparative Modeling Known Structures (Templates) Target Sequence Template Selection Protein Data Bank PDB Alignment Template - Target Structure modeling Structure Evaluation & Assessment Database of templates Homology Model(s) Separate into single chains Remove bad structures (models) Create BLASTable database or fold library (profiles, HMMs) Comparative Modeling Known Structures (Templates) Template selection: Target Sequence Template Selection Alignment Template - Target Structure Evaluation & Assessment 1. Sequence Similarity / Fold recognition 2. Structure quality (resolution, experimental method) Structure modeling Homology Model(s) 3. Experimental conditions (ligands and cofactors)

10 Comparative Modeling Known Structures (Templates) Target Sequence Template Selection Multiple sequence alignment for pairs > 40% identity or Use structural alignment of templates to guide sequence alignment of target or Use separate profiles for template and targets Alignment Template - Target Structure modeling Homology Model(s) Structure Evaluation & Assessment Comparative Modeling Known Structures (Templates) Target Sequence Template Selection Alignment Template - Target Structure Evaluation & Assessment Errors in template selection or alignment result in bad models Structure modeling Homology Model(s) iterative cycles of alignment, modeling and evaluation Built many models, choose best.

11 Comparative Modeling Known Structures (Templates) Target Sequence Template Selection Alignment Template - Target Structure Evaluation & Assessment I. Manual Model building Structure modeling II. Template based fragment assembly Composer (Sybyl, Tripos) SWISS-MODEL Homology Model(s) III. Satisfaction of spatial restraints Modeller (Insight II, MSI) CPH-Models I. Manual Modeling [ ]

12 II. Template based fragment assembly Find structurally conserved core regions II. Template based fragment assembly Build model core by averaging core template backbone atoms (weighted by local sequence similarity with the target sequence). Leave non-conserved regions (loops) for later.

13 II. Template based fragment assembly Loop (insertion) modeling Use the spare part algorithm to find compatible fragments in a Loop- Database, or ab-initio rebuilding (e.g. Monte Carlo, MD, GA, etc.) to build missing loops. II. Template based fragment assembly Side Chain placement Find the most probable side chain conformation, using homologues structure information back-bone dependent rotamer libraries energetic and packing criteria

14 II. Template based fragment assembly Rotamer Libraries Only a small fraction of all possible side chain conformations is observed in experimental structures Rotamer libraries provide an ensemble of likely conformations The propensity of rotamers depends on the backbone geometry: Backbone-dependent rotamer libraries Phe,Tyr, His g+ p(g+ phi) p(g+ psi) trans p(t phi) p(t psi) p(g- phi) p(g- psi) g-

15 II. Template based fragment assembly Energy minimization modeling method will produce unfavorable contacts and bonds Energy minimization is used to regularize local bond and angle geometry Relax close contacts and geometric strain extensive energy minimization will move coordinates away from real structure keep it to a minimum SWISS-MODEL is using GROMOS 96 force field for a steepest descent III. Satisfaction of Spatial restraints Alignment of target sequence with templates Extraction of spatial restraints from templates Modeling by satisfaction of spatial restraints M A T E A F T Q S G

16 III. Satisfaction of Spatial restraints Some features of a protein structure: R resolution of X-ray experiment r amino acid residue type Φ, Ψ main chain angles t secondary structure class M main chain conformation class Χ i,, c i side chain dihedral angle class a residue solvent accessibility s residue neighborhood difference d C a -C a distance d difference between two C a -C a distances III. Satisfaction of Spatial restraints Feature properties can be associated with a protein (e.g. X-ray resolution) residues (e.g. solvent accessibility) pairs of residues (e.g. C a -C a distance) other features (e.g. main chain classes) How can we derive modeling restraints from this data? A restraint is defined as probability density function (pdf) p(x): p( x1 x < x2) = x1 x2 p( x) dx with p( x) dx = 1 p( x) > 0

17 III. Satisfaction of Spatial restraints Derive pdfs from frequency tables by smoothing: a) 11 Cys residues Chi-1 angles b) smoothed distribution from a) c) 297 Cys Chi-1 angles as control III. Satisfaction of Spatial restraints Combine basis pdfs to molecular probability density functions 0.2 < s' < < s' ' < < s' < < s'' < < s' < < s'' < 0.4

18 III. Satisfaction of Spatial restraints Satisfaction of spatial restraints Find the protein model with the highest probability Variable target function: Start with a linear conformation model or a model close to the template conformation At first, use only local restraints minimize some steps using a conjugate gradient optimization repeat with introducing more and more long range restraints until all restraints are used Model Accuracy Evaluation CASP Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction EVA Evaluation of Automatic protein structure prediction [ Burkhard Rost, Andrej Sali, ]

19 Evaluation of Automatic protein structure prediction [ Burkhard Rost, Andrej Sali, ] New PDB Release Prediction Servers Target Sequence MNIFEMLRID EGLRLKIYKD TEGYYTIGIG HLLTKSPSLN AAKSELDKAI GRNCNGVITK 2 1 e.g. 3 Evaluation of prediction accuracy Typical types of errors Sequence alignment errors. Loops which cannot be rebuilt. Inappropriate template selection. Structural rearrangements.

20 Empirical Force Fields e.g. GROMOS, CHARMM, AMBER,... Which type of errors in a protein structure can you identify by an empirical force filed? Which type of errors are not recognized? Statistical Methods Ramachandran Plot of backbone angles (ϕ,ψ) favored regions generously allowed regions disallowed regions Amino acids with special properties: PRO: ϕ = 60º GLY () Useful to identify regions with errors in backbone geometry

21 1D - 3D Checks Probability for a feature to occur in a given environment, e.g. Solvent exposed / buried Hydrophobic / polar environment Electrostatic interactions Secondary structure etc. Statistical Mean Force Potentials A I * + II III Val13 Met80 Phe134 Ala182 B *, Met80 +, Ile86 I, Val13 III, Ala182 II, Phe134

22 Atom Type Definitions Statistical Mean Force Potentials MFP kcal/mol Methyl-Methyl pairs Cysteine S-S-pairs Distance Å Distance Å

23 ANOLEA : (Atomic Non-Local Environment Assessment) ANOLEA Correct Structure: PDB: 1GES Detects local packing errors Errors in alignments Model with wrong alignment:

24 PROCHECK Checks the stereo-chemical quality of a protein structure, producing a number of plots analyzing its overall and residue-by-residue geometry. Covalent geometry Planarity Dihedral angles Chirality Non-bonded interactions Main-chain hydrogen bonds Disulphide bonds Stereochemical parameters Residue-by-residue analysis Laskowski R A, MacArthur M W, Moss D S & Thornton J M (1993). PROCHECK: a program to check the stereochemical quality of protein structures. J. Appl. Cryst., 26, Morris A L, MacArthur M W, Hutchinson E G & Thornton J M (1992). Stereochemical quality of protein structure coordinates. Proteins, 12, WhatCheck / WhatIf WHAT IF I check my structure? Imagine... An everyday situation in a biocomputing lab: "Should they use the structure?" An everyday situation in a crystallography lab: "Should they deposit the structure already?" In a WHAT_CHECK report, each reported fact has an assigned severity: error: severe errors encountered during the analyses. Items marked as errors are considered severe problems requiring immediate attention. warning: Either less severe problems or uncommon structural features. These still need special attention. note: Statistical values, plots, or other verbose results of tests and analyses that have been performed. WHAT IF: A molecular modeling and drug design program. G.Vriend, J. Mol. Graph. (1990) 8, Errors in protein structures. R.W.W. Hooft, G. Vriend, C. Sander, E.E. Abola, Nature (1996) 381,

25 WhatCheck / WhatIf report for a bad model... # 49 # Note: Summary report for users of a structure This is an overall summary of the quality of the structure as compared with current reliable structures. This summary is most useful for biologists seeking a good structure to use for modelling calculations. The second part of the table mostly gives an impression of how well the model conforms to common refinement constraint values. The first part of the table shows a number of constraint-independent quality indicators. Structure Z-scores, positive is better than average: 1st generation packing quality : nd generation packing quality : (bad) Ramachandran plot appearance : chi-1/chi-2 rotamer normality : Backbone conformation : RMS Z-scores, should be close to 1.0: Bond lengths : Bond angles : Omega angle restraints : Side chain planarity : (loose) Improper dihedral distribution : (loose) Inside/Outside distribution : (unusual) All checking tools are happy, so can I believe it now? Models are not experimental facts! Models can be partially inaccurate or sometimes completely wrong! A model is a tool that helps to interpret biochemical data.

26 Some useful Evaluation Tools ANOLEA : (Atomic Non-Local Environment Assessment) ProCheck WhatCheck Verify3D Biotech Validation Suite for Protein Structures Model quality vs. sequence identity Midnight Zone Twilight Zone Save Zone

27 What can models be used for? Discovery of CK2a Inhibitors by in silico docking Homology model of the target molecule: Reference: Discovery of a potent and selective protein kinase CK2 inhibitor by high-throughput docking. Vangrevelinghe E, Zimmermann K, Schoepfer J, Portmann R, Fabbro D, Furet P. Oncology Research, Novartis Pharma, Basle, J Med Chem Jun 19;46(13):

28 Discovery of CK2a Inhibitors by in silico docking In silico docking of a virtual library of compounds Distributed Computing on PC Grid Structural Genomics large scale experimental structure solution projects Goal: Most of the sequences in a genome database should match at least one structure with a sufficient sequence identity allowing for reliable modeling. The modeling error determines selection of targets for structural genomics. Range of sequence space that can be modeled with acceptable accuracy.

29 Structural Genomics Target Selection Protein Modeling Resources SWISS-MODEL Modeller WhatIf 3D-JIGSAW CPHmodels SDSC